Variational Weighting for Kernel Density Ratios
Authors: Sangwoong Yoon, Frank Park, Gunsu YUN, Iljung Kim, Yung-Kyun Noh
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments, We first demonstrate in Fig. 3 how the use of VWKDE alters log probability density ratio (LPDR) and K-L divergence toward a better estimation. Table 1: Performances for defect surface detection (left) and defect localization (right). |
| Researcher Affiliation | Collaboration | Sangwoong Yoon Korea Institute for Advanced Study swyoon@kias.re.kr Frank C. Park Seoul National University / Saige Research fcp@snu.ac.kr Gunsu Yun POSTECH gunsu@postech.ac.kr Iljung Kim Hanyang University iljung0810@hanyang.ac.kr Yung-Kyun Noh Hanyang University / Korea Institute for Advanced Study nohyung@hanyang.ac.kr |
| Pseudocode | Yes | Algorithm 1 Model-free and Algorithm 2 Model-based |
| Open Source Code | Yes | Code is available at https://github.com/swyoon/variationally-weighted-kernel-density-estimation |
| Open Datasets | Yes | For the evaluation of the algorithm, we use a publicly available dataset for surface inspection: DAGM2. The dataset contains six distinct types of normal and defective surfaces. and Data access: https://hci.iwr.uni-heidelberg.de/node/3616 |
| Dataset Splits | No | Appendix H states: 'There are 1,150 images per class, half of which is for training and the remaining is for testing.' This specifies training and testing, but no explicit separate validation split is mentioned. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software components, programming languages, or libraries used in the experiments. |
| Experiment Setup | Yes | The structure of our CNN is Conv(20)-Conv(20)-Max Pool-Conv(20)-Conv(20)Max Pool-FC(20)-Drop Out-FC(1), where Conv is a 3x3 convolution layer, Max Pool is a 2x2 max pooling layer, FC is a fully connected layer, and Drop Out is a drop out operation with probability 0.5. We use binary cross entropy loss for objective function and ADAM for optimization. |